Clinically Meaningful Explainability for NeuroAI: An ethical, technical, and clinical perspective

Explainable AI (XAI) is rarely used in practice for brain stimulation devices, despite being promoted as a way to build trust and transparency. The paper emphasizes that current XAI methods don't match what clinicians actually need, highlighting a gap between theory and real-world application.

Clinicians need actionable explanations, not exhaustive technical details. Rather than comprehensive technical transparency, doctors prioritize clear input-output relationships and feature importance—information they can directly use to make treatment decisions. Overwhelming clinicians with full technical details can actually harm decision-making rather than help it.

"Clinically Meaningful Explainability" (CME) should replace the pursuit of complete technical transparency in brain-computer interfaces and neurostimulation devices. The paper argues that prioritizing clarity and relevance over technical completeness is more ethical and practical for patient care.

Better interface design is critical for responsible AI in neurotechnology. The authors propose NeuroXplain, a reference architecture that translates CME into specific design recommendations—showing that explainability isn't just a technical problem but a design and communication challenge.

Regulatory frameworks need to account for clinician needs, not just technical standards. The paper calls on stakeholders and regulators to ensure that explainability requirements for neurotechnology devices are grounded in what clinicians actually need to safely and effectively treat patients.